Row 5774

Row ID: 5774 | Dataset Entry | Axioma AXP Content Repository

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The output of a GNN will naturally be features for all the nodes. Assuming your edges aren't also being learned, you just need to write a helper function to recreate the TF graph tensor from the node features. Presumably you created the graph tensor in the first place, so you just make it again except using the GNN output as your node features. That's if I understand you right.

But it sounds like you don't really need it in graph tensor form anyway? If you just want the answers, use the GNN output directly. You'll have one feature vector per node, shape= (nodes,features).

FieldValue
text The output of a GNN will naturally be features for all the nodes. Assuming your edges aren't also being learned, you just need to write a helper function to recreate the TF graph tensor from the node features. Presumably you created the graph tensor in the first place, so you just make it again except using the GNN output as your node features. That's if I understand you right. But it sounds like you don't really need it in graph tensor form anyway? If you just want the answers, use the GNN…
label r/tensorflow
dataType comment
communityName r/tensorflow
datetime 2024-05-03
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Raw Record

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  "text": "The output of a GNN will naturally be features for all the nodes.  Assuming your edges aren't also being learned, you just need to write a helper function to recreate the TF graph tensor from the node features.  Presumably you created the graph tensor in the first place, so you just make it again except using the GNN output as your node features.  That's if I understand you right.\n\nBut it sounds like you don't really need it in graph tensor form anyway?  If you just want the answers, use the GNN output directly.  You'll have one feature vector per node, shape= (nodes,features).",
  "label": "r/tensorflow",
  "dataType": "comment",
  "communityName": "r/tensorflow",
  "datetime": "2024-05-03",
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}
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